@inproceedings{khatuya-etal-2025-label,
    title = "Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification",
    author = "Khatuya, Subhendu  and
      Naidu, Shashwat  and
      Ghosh, Saptarshi  and
      Goyal, Pawan  and
      Ganguly, Niloy",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/acl25-workshop-ingestion/2025.findings-acl.1145/",
    pages = "22286--22298",
    ISBN = "979-8-89176-256-5",
    abstract = "The explosion of textual data has made manual document classification increasingly challenging. To address this, we introduce a robust, efficient domain-agnostic generative model framework for multi-label text classification. Instead of treating labels as mere atomic symbols, our approach utilizes predefined label descriptions and is trained to generate these descriptions based on the input text. During inference, the generated descriptions are matched to the predefined labels using a finetuned sentence transformer. We integrate this with a dual-objective loss function, combining cross-entropy loss and cosine similarity of the generated sentences with the predefined target descriptions, ensuring both semantic alignment and accuracy. Our proposed model LAGAMC stands out for its parameter efficiency and versatility across diverse datasets, making it well-suited for practical applications. We demonstrate the effectiveness of our proposed model by achieving new state-of-the-art performances across all evaluated datasets, surpassing several strong baselines. We achieve improvements of 13.94 {\%} in Micro-F1 and 24.85 {\%} in Macro-F1 compared to the closest baseline across all datasets."
}